1. Field of the Invention
Embodiments herein relate to systems and methods for determining potential health risks by analyzing electrocardiograms (ECG).
2. Background Art
Heart rhythm abnormalities, referred to as “arrhythmias” and originating from both the atria and ventricles, constitute a predisposing condition leading to significant morbidity and mortality in the U.S. population. Atrial fibrillation affects 2.2 million U.S. citizens and accounts for 500,000 hospitalizations annually. Sudden cardiac death due to ventricular arrhythmias accounts for 310,000 U.S. deaths each year. Thus, there is a great need to improve arrhythmia risk assessment, which can lead to better diagnosis of underlying disease and help to guide therapy.
The public health impact of arrhythmias is underscored by the prevalence of heart failure. This condition in which atrial and ventricular arrhythmias co-exist affects over five million Americans, with hospitalization of more than one million patients for decompensated heart failure yearly. These individuals experience a high degree of ventricular ectopy and spontaneous ventricular arrhythmias. Sudden cardiac death constitutes a high proportion of deaths in the heart failure population (58% in New York Heart Association [NYHA] class III and 33% in NYHA class IV). However, no standard electrocardiographic markers, including ventricular ectopy or arrhythmias, have proven to be reliable indicators of life-threatening cardiac arrhythmias.
Considerable evidence indicates that analysis of subtle variations in ECG signal morphology, including T-wave heterogeneity, T-wave variability, and T-wave alternans (TWA) may reveal arrhythmia risk. However, intrinsic morphology differences among ECG signals in the standard leads may mask arrhythmogenic ECG morphology changes. Complex influences including impedance and ECG vector cancellation of electrocardiographic signals contribute to differences in the projected amplitude of the signals to the body surface. Thus, microvolt levels of ECG morphology changes that are associated with disease states such as ischemic episodes, acute coronary syndrome, or heart failure may be difficult and imprecise to detect.